---
title: "基于包装器的高效特征选择改进风能和太阳能预测：一项实证研究"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.14024v1"
permalink: "https://feed.astralor.com/items/itm_41ead1c26296304dc9f7007c"
published: "2026-07-15T16:55:42.000Z"
score: 71
channel: "tech"
tags: ["CSFS","SFS","包装器方法","可再生能源预测","特征选择"]
---

# 基于包装器的高效特征选择改进风能和太阳能预测：一项实证研究

## AI 点评

> 这篇论文对从事可再生能源预测或自动化机器学习特征选择的研究者和工程师很有参考价值。CSFS的聚类加速思路简单有效，且代码开源，容易复现。如果你正为风电或光伏预测模型选择特征而头疼，本文的对比实验能直接指导你选择合适的特征选择策略。我觉得最有意思的是，作者没有盲目追求最新复杂方法，而是回归基础，用工程实用的方式解决了计算效率问题。

## 速览

本文针对可再生能源预测中特征选择方法不足的问题，提出了一种新的基于聚类的序列特征选择方法（CSFS）。CSFS是一种模型无关、自动且高效的特征选择包装器，通过聚类降低计算复杂度。在风力涡轮机功率曲线建模和光伏功率预测两个实际案例中，CSFS在保持与标准序列特征选择（SFS）相当预测性能的同时，平均降低21%的计算成本。论文提供了开源实现，并对比了多种特征选择技术，证明包装器方法整体优于过滤法和嵌入法。

## 关键点

- 提出CSFS，一种基于聚类的包装器特征选择方法，适用于可再生能源预测。
- CSFS在保持预测性能的同时，平均减少21%计算时间。
- 通过两项文献综述和实证对比，揭示现有特征选择方法的不足。

## 全文

With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions. Therefore, reliable prediction of current and future energy production is essential. In this paper, we report findings from two structured literature reviews on real-world renewable energy prediction tasks: wind turbine power curve modeling and photovoltaic power prediction. For the former, we conducted a comprehensive literature review ourselves, while for the latter, we synthesize the key findings regarding frequently selected input features based on an existing survey. Across both domains, our analysis reveals that despite the large number of available monitoring and environmental variables, only limited or unsystematic methods for feature selection exist. To address this gap, we propose Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic, clustering-based wrapper method for automatic, efficient, and reliable feature selection in renewable energy prediction pipelines. To support reproducibility and reuse, we provide an open-source implementation of CSFS on GitHub. We empirically evaluate the proposed approach on both use cases and compare it with established feature selection techniques such as wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance. The results show that the wrapper-based methods overall provide better-performing selections of features. CSFS achieves a predictive performance comparable to SFS while reducing computational cost by an average of 21%.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_41ead1c26296304dc9f7007c